Sample Selector

Sample Selector is a tool for creating and editing
samples, or groups of data you compare across—they're
not "samples" in the statistical sense, but more like filters.

By default, a single sample exists: "All Data". With the Sample
Selector, you can create new samples to organize your data.

You can use samples to:

Compare across conditions

Narrow the scope of data analysis to a specific time range,
set of students, problem category, or unit of a curriculum (for example)

A sample is composed of one or more filters, specific
conditions that narrow down your sample.

Creating a sample

The general process for creating a sample is to:

Add a filter from the categories at the left to the composition
area at the right

Modify the filter to select the subset of data you're interested
in, saving it when done

View the sample preview table to see the effect of adding your filter,
making sure you don't have an empty set (ie, a filter or combination
of filters that exclude all transactions).

Name and describe the sample

Decide whether to share the sample with others who can view the
dataset

Save the sample

The effect of multiple filters

DataShop interprets each filter after the first as an additional
restriction on the data that is included in the sample. This is also known
as a logical "AND". You can see the results of multiple filters in the
sample preview as soon as all filters are "saved".

DataShop Home (not logged in)

Welcome to Datashop!

Log in to get started. DataShop supports InCommon and Google single sign-on (SSO) options. You will be able to link your DataShop account with your university id or Google account. The first time you log in you will be asked to create a DataShop account.

Datasets in DataShop are grouped into a few categories:

Public Datasets are datasets you
(or anyone) can access once you log in. You can use any of the DataShop tools
on data in these datasets.

Private Datasets are datasets that
can only be accessed once you've gotten permission from the Principal Investigator
(PI) of the project.

Projects are groupings of datasets. Each
project name appears above its table.

indicates a public project.

indicates that papers that
reference these data have been published by members of this project.
Mouse-over the star to see the number of papers.

Dataset status, the last column of each
dataset table, is often one of the following:

complete: data collection is complete

on-going: data collection is on-going

files-only: there is no
tutor interaction data in this dataset, only files and
papers